Pathological Voice Analysis and Classification Based on Empirical Mode Decomposition

GASTÓN SCHLOTTHAUER; MARÍA EUGENIA TORRES; HUGO LEONARDO RUFINER

Development of Multimodal Interfaces: Active Listening and Synchrony (LNCS 5967)

Springer

Lugar: Berlin; Año: 2010; p. 364 - 381

Empirical mode decomposition (EMD) is an algorithm for signal analysis recently introduced by Huang. It is a completely datadriven non-linear method for the decomposition of a signal into AM - FM components. In this paper two new EMD-based methods for the analysis and classification of pathological voices are presented. They are applied to speech signals corresponding to real and simulated sustained vowels. We rst introduce a method that allows the robust extraction of the fundamental frequency of sustained vowels. Its determination is crucial for pathological voice analysis and diagnosis. This new method is based on the ensemble empirical mode decomposition (EEMD) algorithm and its performance is compared with others from the state of the art. As a second EMD-based tool, we explore spectral properties of the intrinsic mode functions and apply them to the classication of normal and pathological sustained vowels. We show that just using a basic pattern classication algorithm, the selected spectral features of only three modes are enough to discriminate between normal and pathological voices.